Hire Lens Application Tracking System

Abstract

1. Introduction

The Application Tracking System (ATS) is designed to automate the resume screening process by analyzing job descriptions and candidate resumes to provide a relevance score. By leveraging natural language processing (NLP) and machine learning techniques, the system enhances recruitment efficiency by reducing manual effort and ensuring an objective candidate evaluation process.

2. Objective

A command-line Application Tracking System (ATS) that automates resume screening by analyzing job descriptions and ranking candidate resumes using NLP and machine learning techniques.

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Resume Parsing: Extracts text from PDF and DOCX resumes.
• Text Preprocessing: Tokenizes, removes stopwords, and lemmatizes text for better analysis.
• Similarity Calculation: Uses TF-IDF vectorization and cosine similarity to rank resumes.
• Employer Mode: Enables recruiters to input job descriptions for candidate evaluation.
• Candidate Mode: Allows applicants to submit resumes and receive relevance scores.
• Jupyter Notebook-Based Interface: Provides an interactive and structured environment for execution.

4. Technology Stack

• Python – Core programming language used for processing and logic.
• Jupyter Notebook – Provides an interactive interface for executing the ATS system.
• PyPDF2 & python-docx – Extracts text from PDF and DOCX resume files.
• NLTK (Natural Language Toolkit) – Handles text tokenization, stopword removal, and lemmatization.
• Scikit-learn – Implements TF-IDF vectorization and cosine similarity for ranking resumes.
• Pyfiglet & Termcolor – Enhances CLI output with ASCII formatting and color styling.
• OS Module – Manages file handling operations.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AdityaBhatt3010/HireLens-Application-Tracking-System.git
cd HireLens-Application-Tracking-System

1. Clone the repository:
2. Install dependencies:
3. Open the Jupyter Notebook:
4. Run the notebook cells sequentially.
5. Choose Employer Mode to input job descriptions or Candidate Mode to evaluate resumes.
6. Follow the on-screen prompts to analyze resume relevance scores.

git clone https://github.com/your-repo/ATS.git
   cd ATS

pip install -r requirements.txt

jupyter notebook ATS.ipynb

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by AdityaBhatt3010 and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.